experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Update usage of tf.keras.losses.BinaryCrossEntropy

Browse Source 
PiperOrigin-RevId: 347092623
Change-Id: I956364fdda51f099f950faf411612b8604d7d194
This commit is contained in:
Meghna Natraj 2020-12-11 15:56:24 -08:00 committed by TensorFlower Gardener
parent 5a51f9bbed
commit 28c7e4d9f2
1 changed files with 107 additions and 102 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

209
tensorflow/python/keras/losses.py
View File

@ -12,8 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Built-in loss functions.
"""
"""Built-in loss functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
@ -92,8 +91,8 @@ class Loss(object):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: Optional name for the op.
"""
losses_utils.ReductionV2.validate(reduction)
@ -122,15 +121,15 @@ class Loss(object):
sparse loss functions such as sparse categorical crossentropy where
shape = `[batch_size, d0, .. dN-1]`
y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`
sample_weight: Optional `sample_weight` acts as a
coefficient for the loss. If a scalar is provided, then the loss is
simply scaled by the given value. If `sample_weight` is a tensor of size
`[batch_size]`, then the total loss for each sample of the batch is
rescaled by the corresponding element in the `sample_weight` vector. If
the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be
broadcasted to this shape), then each loss element of `y_pred` is scaled
sample_weight: Optional `sample_weight` acts as a coefficient for the
loss. If a scalar is provided, then the loss is simply scaled by the
given value. If `sample_weight` is a tensor of size `[batch_size]`, then
the total loss for each sample of the batch is rescaled by the
corresponding element in the `sample_weight` vector. If the shape of
`sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted to
this shape), then each loss element of `y_pred` is scaled
by the corresponding value of `sample_weight`. (Note on`dN-1`: all loss
functions reduce by 1 dimension, usually axis=-1.)
functions reduce by 1 dimension, usually axis=-1.)
Returns:
Weighted loss float `Tensor`. If `reduction` is `NONE`, this has
@ -230,8 +229,8 @@ class LossFunctionWrapper(Loss):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: (Optional) name for the loss.
**kwargs: The keyword arguments that are passed on to `fn`.
"""
@ -250,8 +249,7 @@ class LossFunctionWrapper(Loss):
Loss values per sample.
"""
if tensor_util.is_tensor(y_pred) and tensor_util.is_tensor(y_true):
y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
y_pred, y_true)
y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(y_pred, y_true)
ag_fn = autograph.tf_convert(self.fn, ag_ctx.control_status_ctx())
return ag_fn(y_true, y_pred, **self._fn_kwargs)
@ -314,8 +312,8 @@ class MeanSquaredError(LossFunctionWrapper):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: Optional name for the op. Defaults to 'mean_squared_error'.
"""
super(MeanSquaredError, self).__init__(
@ -373,8 +371,8 @@ class MeanAbsoluteError(LossFunctionWrapper):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: Optional name for the op. Defaults to 'mean_absolute_error'.
"""
super(MeanAbsoluteError, self).__init__(
@ -433,8 +431,8 @@ class MeanAbsolutePercentageError(LossFunctionWrapper):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: Optional name for the op. Defaults to
'mean_absolute_percentage_error'.
"""
@ -494,8 +492,8 @@ class MeanSquaredLogarithmicError(LossFunctionWrapper):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: Optional name for the op. Defaults to
'mean_squared_logarithmic_error'.
"""
@ -507,44 +505,64 @@ class MeanSquaredLogarithmicError(LossFunctionWrapper):
class BinaryCrossentropy(LossFunctionWrapper):
"""Computes the cross-entropy loss between true labels and predicted labels.
Use this cross-entropy loss when there are only two label classes (assumed to
be 0 and 1). For each example, there should be a single floating-point value
per prediction.
Use this cross-entropy loss for binary (0 or 1) classification applications.
The loss function requires the following inputs:
In the snippet below, each of the four examples has only a single
floating-pointing value, and both `y_pred` and `y_true` have the shape
`[batch_size]`.
- `y_true` (true label): This is either 0 or 1.
- `y_pred` (predicted value): This is the model's prediction, i.e, a single
floating-point value which either represents a
[logit](https://en.wikipedia.org/wiki/Logit), (i.e, value in [-inf, inf]
when `from_logits=True`) or a probability (i.e, value in [0., 1.] when
`from_logits=False`).
Standalone usage:
**Recommended Usage:** (set `from_logits=True`)
>>> y_true = [[0., 1.], [0., 0.]]
>>> y_pred = [[0.6, 0.4], [0.4, 0.6]]
>>> # Using 'auto'/'sum_over_batch_size' reduction type.
>>> bce = tf.keras.losses.BinaryCrossentropy()
>>> bce(y_true, y_pred).numpy()
0.815
>>> # Calling with 'sample_weight'.
>>> bce(y_true, y_pred, sample_weight=[1, 0]).numpy()
0.458
>>> # Using 'sum' reduction type.
>>> bce = tf.keras.losses.BinaryCrossentropy(
... reduction=tf.keras.losses.Reduction.SUM)
>>> bce(y_true, y_pred).numpy()
1.630
>>> # Using 'none' reduction type.
>>> bce = tf.keras.losses.BinaryCrossentropy(
... reduction=tf.keras.losses.Reduction.NONE)
>>> bce(y_true, y_pred).numpy()
array([0.916 , 0.714], dtype=float32)
Usage with the `tf.keras` API:
With `tf.keras` API:
```python
model.compile(optimizer='sgd', loss=tf.keras.losses.BinaryCrossentropy())
model.compile(
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
....
)
```
As a standalone function:
>>> # Example 1: (batch_size = 1, number of samples = 4)
>>> y_true = [0, 1, 0, 0]
>>> y_pred = [-18.6, 0.51, 2.94, -12.8]
>>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)
>>> bce(y_true, y_pred).numpy()
0.865
>>> # Example 2: (batch_size = 2, number of samples = 4)
>>> y_true = [[0, 1], [0, 0]]
>>> y_pred = [[-18.6, 0.51], [2.94, -12.8]]
>>> # Using default 'auto'/'sum_over_batch_size' reduction type.
>>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)
>>> bce(y_true, y_pred).numpy()
0.865
>>> # Using 'sample_weight' attribute
>>> bce(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()
0.243
>>> # Using 'sum' reduction` type.
>>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True,
... reduction=tf.keras.losses.Reduction.SUM)
>>> bce(y_true, y_pred).numpy()
1.730
>>> # Using 'none' reduction type.
>>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True,
... reduction=tf.keras.losses.Reduction.NONE)
>>> bce(y_true, y_pred).numpy()
array([0.235, 1.496], dtype=float32)
**Default Usage:** (set `from_logits=False`)
>>> # Make the following updates to the above "Recommended Usage" section
>>> # 1. Set `from_logits=False`
>>> tf.keras.losses.BinaryCrossentropy() # OR ...('from_logits=False')
>>> # 2. Update `y_pred` to use probabilities instead of logits
>>> y_pred = [0.6, 0.3, 0.2, 0.8] # OR [[0.6, 0.3], [0.2, 0.8]]
"""
def __init__(self,
@ -570,8 +588,8 @@ class BinaryCrossentropy(LossFunctionWrapper):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: (Optional) Name for the op. Defaults to 'binary_crossentropy'.
"""
super(BinaryCrossentropy, self).__init__(
@ -650,8 +668,8 @@ class CategoricalCrossentropy(LossFunctionWrapper):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: Optional name for the op. Defaults to 'categorical_crossentropy'.
"""
super(CategoricalCrossentropy, self).__init__(
@ -727,8 +745,8 @@ class SparseCategoricalCrossentropy(LossFunctionWrapper):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: Optional name for the op. Defaults to
'sparse_categorical_crossentropy'.
"""
@ -791,8 +809,8 @@ class Hinge(LossFunctionWrapper):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: Optional name for the op. Defaults to 'hinge'.
"""
super(Hinge, self).__init__(hinge, name=name, reduction=reduction)
@ -852,8 +870,8 @@ class SquaredHinge(LossFunctionWrapper):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: Optional name for the op. Defaults to 'squared_hinge'.
"""
super(SquaredHinge, self).__init__(
@ -912,8 +930,8 @@ class CategoricalHinge(LossFunctionWrapper):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: Optional name for the op. Defaults to 'categorical_hinge'.
"""
super(CategoricalHinge, self).__init__(
@ -969,8 +987,8 @@ class Poisson(LossFunctionWrapper):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: Optional name for the op. Defaults to 'poisson'.
"""
super(Poisson, self).__init__(poisson, name=name, reduction=reduction)
@ -1026,8 +1044,8 @@ class LogCosh(LossFunctionWrapper):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: Optional name for the op. Defaults to 'log_cosh'.
"""
super(LogCosh, self).__init__(log_cosh, name=name, reduction=reduction)
@ -1086,8 +1104,8 @@ class KLDivergence(LossFunctionWrapper):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: Optional name for the op. Defaults to 'kl_divergence'.
"""
super(KLDivergence, self).__init__(
@ -1154,20 +1172,17 @@ class Huber(LossFunctionWrapper):
`tf.distribute.Strategy`, outside of built-in training loops such as
`tf.keras` `compile` and `fit`, using `AUTO` or `SUM_OVER_BATCH_SIZE`
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training)
for more details.
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name: Optional name for the op. Defaults to 'huber_loss'.
"""
super(Huber, self).__init__(
huber, name=name, reduction=reduction, delta=delta)
@keras_export('keras.metrics.mean_squared_error',
'keras.metrics.mse',
'keras.metrics.MSE',
'keras.losses.mean_squared_error',
'keras.losses.mse',
'keras.losses.MSE')
@keras_export('keras.metrics.mean_squared_error', 'keras.metrics.mse',
'keras.metrics.MSE', 'keras.losses.mean_squared_error',
'keras.losses.mse', 'keras.losses.MSE')
@dispatch.add_dispatch_support
def mean_squared_error(y_true, y_pred):
"""Computes the mean squared error between labels and predictions.
@ -1198,12 +1213,9 @@ def mean_squared_error(y_true, y_pred):
return K.mean(math_ops.squared_difference(y_pred, y_true), axis=-1)
@keras_export('keras.metrics.mean_absolute_error',
'keras.metrics.mae',
'keras.metrics.MAE',
'keras.losses.mean_absolute_error',
'keras.losses.mae',
'keras.losses.MAE')
@keras_export('keras.metrics.mean_absolute_error', 'keras.metrics.mae',
'keras.metrics.MAE', 'keras.losses.mean_absolute_error',
'keras.losses.mae', 'keras.losses.MAE')
@dispatch.add_dispatch_support
def mean_absolute_error(y_true, y_pred):
"""Computes the mean absolute error between labels and predictions.
@ -1232,11 +1244,9 @@ def mean_absolute_error(y_true, y_pred):
@keras_export('keras.metrics.mean_absolute_percentage_error',
'keras.metrics.mape',
'keras.metrics.MAPE',
'keras.metrics.mape', 'keras.metrics.MAPE',
'keras.losses.mean_absolute_percentage_error',
'keras.losses.mape',
'keras.losses.MAPE')
'keras.losses.mape', 'keras.losses.MAPE')
@dispatch.add_dispatch_support
def mean_absolute_percentage_error(y_true, y_pred):
"""Computes the mean absolute percentage error between `y_true` and `y_pred`.
@ -1269,11 +1279,9 @@ def mean_absolute_percentage_error(y_true, y_pred):
@keras_export('keras.metrics.mean_squared_logarithmic_error',
'keras.metrics.msle',
'keras.metrics.MSLE',
'keras.metrics.msle', 'keras.metrics.MSLE',
'keras.losses.mean_squared_logarithmic_error',
'keras.losses.msle',
'keras.losses.MSLE')
'keras.losses.msle', 'keras.losses.MSLE')
@dispatch.add_dispatch_support
def mean_squared_logarithmic_error(y_true, y_pred):
"""Computes the mean squared logarithmic error between `y_true` and `y_pred`.
@ -1609,12 +1617,9 @@ def binary_crossentropy(y_true, y_pred, from_logits=False, label_smoothing=0):
@keras_export('keras.metrics.kl_divergence',
'keras.metrics.kullback_leibler_divergence',
'keras.metrics.kld',
'keras.metrics.KLD',
'keras.losses.kl_divergence',
'keras.losses.kullback_leibler_divergence',
'keras.losses.kld',
'keras.metrics.kullback_leibler_divergence', 'keras.metrics.kld',
'keras.metrics.KLD', 'keras.losses.kl_divergence',
'keras.losses.kullback_leibler_divergence', 'keras.losses.kld',
'keras.losses.KLD')
@dispatch.add_dispatch_support
def kl_divergence(y_true, y_pred):